This proposal requests a senior scientist award (K05) to support research that has the goal of contributing to an understanding of the processes and structures of human memory and simple decision making. Investigation is planned in five, theoretically related domains. First, three models of choice reaction time will be tested using data from simple perceptual decision tasks and from higher level cognitive tasks. Second, the models' abilities to elucidate the processes by which information is retrieved from long term memory will be examined to see whether the models can be usefully combined with recently developed memory models in such a way as to be empirically testable. Third, the models will be extended to tasks that have more than two response choices, an extension made feasible by today's fast computers, and large experiments will be used to constrain and test the extended models. Fourth, the work with reaction time models will be extended to test the currently leading models of categorization: exemplar models and distance from criterion models. Fifth, we aim to develop models for the tasks used to study implicit memory. Research on implicit memory has usually progressed without any explicit, quantitatively testable models of implicit memory processes. The goal is to raise the level of theoretical debate about implicit memory to a discussion of exactly how priming effects come about. Overall, the proposed research represents the interaction of two methdologies: the development of explicit models of processing and representation and the development of empirical tests and databases for the models. An important theme is the use of new models to serve as competitors for well-established views, with the aim of driving research in new directions. The K05 award will provide additional time for the applicant to broaden his knowledge and skills in the domains of stochastic process modeling, neural processing, and aging (the latter two are the topics of other funded grants). It will also provide additional time for the research on memory models and reaction time models described in this proposal, and for collaborations with Segraves (neural processing), Smith, and Thapar (aging). All of the proposed research is highly relevant to the mission of NIMH. The broad class of models to be examined can be seen as neurally inspired, and the funded neural modeling grant aims at integrating the behavioral models with single cell neural recording data. In addition, although few models have been applied to populations other than college undergraduates, another funded grant will apply the reaction time models to the cognitive deficits that appear with aging. Future applications of well-validated models could help discriminate such issues as whether memory deficits are due to encoding or retrieval problems, to rapid automatic processes or slower, more conscious ones. Reaction time models especially might lead to diagnostic techniques that are non-invasive and relatively inexpensive.